Systems and Methods for Contrastive Learning of Visual Representations

ABSTRACT

Systems, methods, and computer program products for performing semi-supervised contrastive learning of visual representations are provided. For example, the present disclosure provides systems and methods that leverage particular data augmentation schemes and a learnable nonlinear transformation between the representation and the contrastive loss to provide improved visual representations. Further, the present disclosure also provides improvements for semi-supervised contrastive learning. For example, computer-implemented method may include performing semi-supervised contrastive learning based on a set of one or more unlabeled training data, generating an image classification model based on a portion of a plurality of layers in a projection head neural network used in performing the contrastive learning, performing fine-tuning of the image classification model based on a set of one or more labeled training data, and after performing the fine-tuning, distilling the image classification model to a student model comprising a relatively smaller number of parameters than the image classification model.

RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. Pat.Application No. 16/847,163 filed Apr. 13, 2020, which is herebyincorporated by reference in its entirety.

FIELD

The present disclosure generally relates to systems and methods forcontrastive learning of visual representations. More particularly, thepresent disclosure relates to contrastive learning frameworks thatleverage data augmentation and a learnable nonlinear transformationbetween the representation and the contrastive loss to provide improvedvisual representations.

BACKGROUND

Learning effective visual representations without human supervision is alongstanding problem. Most mainstream approaches fall into one of twoclasses: generative or discriminative. Generative approaches learn togenerate or otherwise model pixels in the input space. However,pixel-level generation is computationally expensive and may not benecessary for representation learning. Discriminative approaches learnrepresentations using objective functions similar to those used forsupervised learning, but train networks to perform pretext tasks whereboth the inputs and labels are derived from an unlabeled dataset. Manysuch approaches have relied on heuristics to design pretext tasks. Theseheuristics often limit the generality of the learned representations.

For example, many existing approaches define contrastive predictiontasks by changing the architecture of the model to be learned. Asexamples, Hjelm et al. (2018) and Bachman et al. (2019) achieveglobal-to-local view prediction via constraining the receptive field inthe network architecture, whereas Oord et al. (2018) and Hénaff et al.(2019) achieve neighboring view prediction via a fixed image splittingprocedure and a context aggregation network. However, these customarchitectures add additional complexity and reduce the flexibilityand/or applicability of the resulting model.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or can be learned fromthe description, or can be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to acomputer-implemented method to perform semi-supervised contrastivelearning of visual representations. The method includes, obtaining atraining image in a set of one or more unlabeled training images,performing a plurality of first augmentation operations on the trainingimage to obtain a first augmented image, separate from performing theplurality of first augmentation operations, performing a plurality ofsecond augmentation operations on the training image to obtain a secondaugmented image, respectively processing, with a base encoder neuralnetwork, the first augmented image and the second augmented image torespectively generate a first intermediate representation for the firstaugmented image and a second intermediate representation for the secondaugmented image, respectively processing, with a projection head neuralnetwork comprising a plurality of layers, the first intermediaterepresentation and the second intermediate representation torespectively generate a first projected representation for the firstaugmented image and a second projected representation for the secondaugmented image, evaluating a loss function that evaluates a differencebetween the first projected representation and the second projectedrepresentation, modifying one or more values of one or more parametersof one or both of the base encoder neural network and the projectionhead neural network based at least in part on the loss function, aftersaid modifying, generating an image classification model from the baseencoder neural network and the projection head neural network, the imageclassification model comprising some but not all of the plurality oflayers of the projection head neural network, performing fine-tuning ofthe image classification model based on a set of labeled images, andafter performing the fine-tuning, performing distillation training usingthe set of unlabeled training images, wherein the distillation trainingdistills the image classification model to a student model comprising arelatively smaller number of parameters relative to the imageclassification model.

Another example aspect of the present disclosure is directed to acomputing system to perform semi-supervised contrastive learning ofvisual representations. The computing system includes one or moreprocessors and one or more non-transitory computer-readable media thatcollectively store: an image classification model comprising a baseencoder neural network, one or more projection head neural networklayers, and a classification head, where the base encoder neural networkand the one or more projection head neural network layers have beenpretrained using contrastive learning based on a set of one or moreunlabeled visual data, and where the one or more projection head neuralnetwork layers comprise some but not all of a plurality of projectionhead neural network layers from a projection head neural network, andinstructions that, when executed by the one or more processors, causethe computing system to perform operations that include: performingfine-tuning of the image classification model using a set of one or morelabeled visual data, and after performing the fine-tuning of the imageclassification model, performing distillation training using the one ormore projection head neural network layers pretrained using contrastivelearning, where the distillation training distills the imageclassification model to a student model comprising a relatively smallernumber of parameters relative to the image classification model.

Another example aspect of the present disclosure is directed to acomputer-implemented method to perform semi-supervised contrastivelearning. The method includes, performing contrastive learning based ona set of one or more unlabeled training data, generating an imageclassification model based on a base encoder neural network used inperforming the contrastive learning and based on some but not all of aplurality of layers in a projection head neural network used inperforming the contrastive learning, performing fine-tuning of the imageclassification model based on a set of one or more labeled trainingdata, and after performing the fine-tuning of the image classificationmodel, performing distillation training using the set of unlabeledtraining data, the distillation training distilling the imageclassification model to a student model comprising a relatively smallernumber of parameters relative to the image classification model.

Other aspects of the present disclosure are directed to various systems,apparatuses, non-transitory computer-readable media, user interfaces,and electronic devices.

These and other features, aspects, and advantages of various embodimentsof the present disclosure will become better understood with referenceto the following description and appended claims. The accompanyingdrawings, which are incorporated in and constitute a part of thisspecification, illustrate example embodiments of the present disclosureand, together with the description, serve to explain the relatedprinciples.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art is set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts an example plot of accuracy of different linearclassifiers trained on representations learned via different techniquesincluding example embodiments of the present disclosure.

FIG. 2A depicts a graphical diagram of a framework for contrastivelearning according to example embodiments of the present disclosure.

FIG. 2B depicts a graphical diagram of an example use of a base encoderneural network trained according to example frameworks according toexample embodiments of the present disclosure.

FIGS. 3A and 3B depict graphical diagrams of example random croppingoutcomes on example images according to example embodiments of thepresent disclosure.

FIG. 4 provides example results of example data augmentation operationsaccording to example embodiments of the present disclosure.

FIG. 5 provides example performance measurements for various dataaugmentation compositions according to example embodiments of thepresent disclosure.

FIGS. 6A and 6B provide histograms that show the effect of example colordistortion augmentation operations according to example embodiments ofthe present disclosure.

FIG. 7 provides linear evaluation results for example models with varieddepth and width according to example embodiments of the presentdisclosure.

FIG. 8 provides linear evaluation results for example models withdifferent projection heads according to example embodiments of thepresent disclosure.

FIG. 9 provides example negative loss functions and their gradientsaccording to example embodiments of the present disclosure.

FIG. 10 provides linear evaluation results for example models withdifferent batch size and number of epochs according to exampleembodiments of the present disclosure.

FIG. 11 depicts a flow diagram of an example method for performingsemi-supervised contrastive learning of visual representations accordingto example embodiments of the present disclosure.

FIG. 12 depicts an example graphical diagram for performing fine-tuningof a classification model with one or more projection head neuralnetwork layers that have been pretrained using contrastive learning,according to example embodiments of the present disclosure.

FIG. 13 depicts an example graphical diagram for performing distillationtraining of a student model based on a fine-tuned classification modelwith one or more projection head neural network layers that have beenpretrained using contrastive learning, according to example embodimentsof the present disclosure.

FIG. 14A depicts a block diagram of an example computing systemaccording to example embodiments of the present disclosure.

FIG. 14B depicts a block diagram of an example computing deviceaccording to example embodiments of the present disclosure.

FIG. 14C depicts a block diagram of an example computing deviceaccording to example embodiments of the present disclosure.

Reference numerals that are repeated across plural figures are intendedto identify the same features in various implementations.

DETAILED DESCRIPTION Overview

Example aspects of the present disclosure are directed to systems andmethods for contrastive learning and semi-supervised contrastivelearning of visual representations. In particular, the presentdisclosure provides systems and methods that leverage particular dataaugmentation schemes and a learnable nonlinear transformation betweenthe representation and the contrastive loss to provide improved visualrepresentations. In contrast to certain existing techniques, thecontrastive self-supervised learning algorithms described herein do notrequire specialized architectures or a memory bank. Some exampleimplementations of the proposed approaches can be referred to as asimple framework for contrastive learning of representations or “SimCLR”and associated configurations for performing semi-supervised contrastivelearning. Further example aspects are described below and provide thefollowing benefits and insights.

One example aspect of the present disclosure is directed to particularcompositions of data augmentations which enable the system to defineeffective predictive tasks. Composition of multiple data augmentationoperations is crucial in defining the contrastive prediction tasks thatyield effective representations. As one example, a combination of randomcrop and color distortions provides particular benefit. In addition,unsupervised contrastive learning benefits from stronger dataaugmentation than supervised learning.

Another example aspect is directed to model frameworks which include alearnable nonlinear transformation between the representation and thecontrastive loss. Introducing a learnable nonlinear transformationbetween the representation and the contrastive loss substantiallyimproves the quality of the learned representations which may be due, atleast in part, to preventing information loss in the representation.

According to another example aspect, specific embodiments are identifiedand evaluated in which contrastive learning benefits from larger batchsizes and more training steps, for example, as compared to supervisedlearning. As one example, representation learning with contrastive crossentropy loss benefits from normalized embeddings and an appropriatelyadjusted temperature parameter. Like supervised learning, contrastivelearning also benefits from deeper and wider networks.

According to yet another example aspect, various examples of performingsemi-supervised contrastive learning are provided. As one example, firsta deep and wide network is pretrained using unlabeled data, next thenetwork is incorporated with some but not all of a plurality ofpretrained projection head neural network layers and is fine-tuned witha small number or fraction of labeled data, and then distillationtraining is performed based on reusing the unlabeled pretraining data todistill the network to a student network that performs one or morespecialized tasks. Such semi-supervised contrastive learning improvesaccuracy and computational efficiency over previously known methods.

Example implementations of the proposed systems are then empiricallyshown to considerably outperform previous methods for self-supervisedand semi-supervised learning on ImageNet. In particular, a linearclassifier trained on self-supervised representations learned by exampleimplementations of the proposed systems and methods achieves 76.5% top-1accuracy, which is a 7% relative improvement over previousstate-of-the-art, matching the performance of a supervised ResNet-50. Asone example, FIG. 1 illustrates ImageNet Top-1 accuracy of linearclassifiers trained on representations learned with differentself-supervised methods (pretrained on ImageNet). The gray crossindicates supervised ResNet-50. Example implementations of the proposedmethod referred to as SimCLR are shown in bold. Further, when fine-tunedon only 1% of the labels, example implementations of the proposedtechniques achieve 85.8% top-5 accuracy, outperforming AlexNet with 100fewer labels. When fine-tuned on other natural image classificationdatasets, SimCLR performs on par with or better than a strong supervisedbaseline on 10 out of 12 datasets.

Thus, the present disclosure provides a simple framework and itsinstantiation for contrastive visual representation learning. Itscomponents are carefully studied and the effects of different designchoices are demonstrated. By combining these findings, the proposedsystems and methods improve considerably over previous methods forself-supervised, semi-supervised, and transfer learning. Specifically,the discussion and results contained herein demonstrate that thecomplexity of some previous methods for self-supervised learning is notnecessary to achieve good performance.

The systems and methods of the present disclosure provide a number oftechnical effects and benefits. As one example, the contrastive learningtechniques described herein can result in models which generate improvedvisual representations. These visual representations can then be used tomake more accurate downstream decisions (e.g., more accurate objectdetections, classifications, segmentations, etc.). Thus, the techniquesdescribed herein result in improved performance of a computer visionsystem.

As another example technical effect and benefit, and in contrast tovarious existing approaches, the contrastive learning techniquesdescribed herein do not require use of a memory bank. By obviating theneed for a dedicated memory bank, the proposed techniques can reducememory load, thereby conserving computing resources such as memoryresources.

As another example technical effect and benefit, and in contrast tovarious existing approaches, the contrastive learning techniquesdescribed herein do not require specialized, custom, or otherwise undulycomplex model architectures to enable contrastive learning. By obviatingthe need for complex architectures, more simplified architectures can beused, resulting in models which run faster (e.g., reduced latency) andconsume fewer computing resources (e.g., reduced usage of processors,memory, network bandwidth, etc.)

With reference now to the Figures, example embodiments of the presentdisclosure will be discussed in further detail.

Example Contrastive Learning Techniques Example Contrastive LearningFramework

Example implementations of the present disclosure learn representationsby maximizing agreement between differently augmented views of the samedata example via a contrastive loss in the latent space. As illustratedin FIG. 2A, an example framework 200 can include the following fourmajor components:

A stochastic data augmentation module (shown generally at 203) thattransforms any given data example (e.g., an input image x shown at 202)randomly resulting in two correlated views of the same example, denoted^(x̃)t and ^(x̃)j, which are shown at 212 and 222, respectively. Theseaugmented images 212 and 222 can be considered as a positive pair.Although the present disclosure focuses on data examples from the imagedomain for ease of explanation, the framework is extensible to dataexamples of different domains as well which are susceptible toaugmentation of some kind, including text and/or audio domains. Exampletypes of images that can be used include video frames, LiDAR pointclouds, computed tomography scans, X-ray images, hyper-spectral images,and/or various other forms of imagery.

In some example implementations, three augmentations can be applied at203: random cropping followed by resize back to the original size,random color distortions, and random Gaussian blur. As shown in thefollowing sections, the combination of random crop and color distortionsignificantly assists in providing a good performance. However, variousother combinations of augmentations can be performed.

A base encoder neural network 204 (represented in notation herein as^(f(^))) that extracts intermediate representation vectors fromaugmented data examples. For example, in the illustration of FIG. 2A,the base encoder neural network 204 has generated intermediaterepresentations 214 and 224 from augmented images 212 and 222,respectively. The example framework 200 allows various choices of thenetwork architecture without any constraints. Some exampleimplementations opt for simplicity and adopt the ResNet architecture (Heet al., 2016) to obtain

$\text{h}_{\text{i}} = f\left( {\widetilde{\text{x}}}_{\text{i}} \right) = \text{ResNet}\left( {\widetilde{\text{x}}}_{\text{i}} \right)\text{where h}_{\text{i}} \in {\mathbb{R}}^{d}$

is the output after the average pooling layer.

A projection head neural network 206 (represented in the notation hereinas g(·))that maps the intermediate representations to finalrepresentations within the space where contrastive loss is applied. Forexample, the projection head neural network 206 has generated finalrepresentations 216 and 226 from the intermediate representations 214and 224, respectively. In some example implementations of the presentdisclosure, the projection head neural network 206 can be a multi-layerperceptron with one hidden layer to obtain z_(t) = g(h_(t)) =W⁽²⁾σ(W⁽¹⁾h_(t)) where σ is a ReLU non-linearity. As shown in thefollowing sections, it is beneficial to define the contrastive loss onfinal representations z_(t)’s rather than intermediate representationsh_(t)’s.

A contrastive loss function can be defined for a contrastive predictiontask. As one example, given a set {x̃_(k)} including a positive pair ofexamples x̃_(t) 212 and x̃_(j) 222, the contrastive prediction task aimsto identify x̃_(j) in {x̃_(k)}_(k=i) for a given x̃_(t), e.g., based onsimilarly between their respective final representations 216 and 226.

In some implementations, to perform training within the illustratedframework, a minibatch of N examples can be randomly sampled and thecontrastive prediction task can be defined on pairs of augmentedexamples derived from the minibatch, resulting in 2N data points. Insome implementations, negative examples are not explicitly sampled.Instead, given a positive pair, the other 2(N - 1) augmented exampleswithin a minibatch can be treated as negative examples. Let

sim(u,v) = u^(τ)v/∥u∥∥v∥

denote the cosine similarity between two vectors u and v. Then oneexample loss function for a positive pair of examples (t, j) can bedefined as

$\begin{matrix}{\mathcal{l}_{i,j} = - \log\frac{\exp\left( {{\text{sim}\left( {z_{i},z_{j}} \right)}/\tau} \right)}{\sum_{k = 1}^{2N}{\mathbb{I}_{\lbrack{k \neq i}\rbrack}\exp\left( {{\text{sim}\left( {z_{i},z_{k}} \right)}/\tau} \right)}},} & \text{­­­(1)}\end{matrix}$

where k = i ∈ {0,1} is an indicator function evaluating to 1 iff k ≠ iand r denotes a temperature parameter. The final loss can be computedacross all positive pairs, both (i,j) and (j,t), in a minibatch. Forconvenience, this loss is referred to further herein as NT-Xent (thenormalized temperature-scaled cross entropy loss).

The below example Algorithm 1 summarizes one example implementation ofthe proposed method:

Algorithm 1 Example Learning Algorithm input: batch size N, temperatureconstant r, structure of f, g, t. for sampled minibatch do for all k ∈{1,...,N} do draw two augmentation functions # the first augmentationx̃_(2k-1) = t(x_(k)) h_(2k-1) = f(x̃_(2k-1)) # representation z_(2k-1) =g(h_(2k-1)) # projection # the second augmentation X_(2k) = t′(x_(k))h_(2k) = f(x̃_(2k)) # representation z_(2k)= g(h_(2k)) # projection endfor for all ∈ {1, …,2N3} and j ∈ {1,...,2N} do # pairwise similarity endfor define f( i,f) as$\mathcal{l}\left( {i,j} \right) = - \log\frac{\exp\left( s_{i,j} \right)}{\sum_{k = 1}^{2N}{\mathbb{I}_{\lbrack{k \neq i}\rbrack}\exp\left( s_{i,k} \right)}}$$L = \frac{1}{2N}{\sum_{k = 1}^{N}\left\lbrack {\mathcal{l}\left( {2k - 1,2k} \right) + \mathcal{l}\left( {2k,2k - 1} \right)} \right\rbrack}$update networks f and g to minimize ℐ end for return encoder networkf(̇̇̇•), and optionally throw away g(•)

FIG. 2B depicts a graphical diagram of an example use of a base encoderneural network trained after it has been trained in the exampleframework shown in FIG. 2A. In particular, the base encoder neuralnetwork 204 has been extracted and an additional task specific model 250has been appended to the base encoder neural network 204. For example,the task specific model 250 can be any kind of model including linearmodels or non-linear models such as neural networks.

The task specific model 250 and/or the base encoder neural network 204can be additionally trained (e.g., “fine-tuned”) on additional trainingdata (e.g., which may be task specific data). The additional trainingcan be, for example, supervised learning training.

After fine-tuning, an additional input 252 can be provided to the baseencoder neural network 204 which can produce an intermediaterepresentation 254. The task-specific model 250 can receive and processthe intermediate representation 254 to generate a task-specificprediction 256. As examples, the task-specific prediction 256 can be aclassification prediction; a detection prediction; a recognitionprediction; a segmentation prediction; and/or other prediction tasks.

Example Training With Large Batch Size

Example implementations of the present disclosure enable training of themodel without use of a memory bank. Instead, in some implementations,the training batch size N can be varied from 256 to 8192. A batch sizeof 8192 provides 16382 negative examples per positive pair from bothaugmentation views. Training with large batch size may be unstable whenusing standard SGD/Momentum with linear learning rate scaling. Tostabilize the training, the LARS optimizer (You et al. 2017) can be usedfor all batch sizes. In some implementations, the model can be trainedwith Cloud TPUs, using 32 to 128 cores depending on the batch size.

Global BN. Standard ResNets use batch normalization. In distributedtraining with data parallelism, the BN mean and variance are typicallyaggregated locally per device. In some example implementations ofcontrastive learning techniques described herein, as positive pairs arecomputed in the same device, the model can exploit the local informationleakage to improve prediction accuracy without improvingrepresentations. For example, this issue can be addressed by aggregatingBN mean and variance over all devices during the training. Otherapproaches include shuffling data examples or replacing BN with layernorm.

Example Evaluation Protocol

This subsection describes the protocol for example empirical studiesdescribed herein, which aim to understand different design choices inthe proposed framework.

Example Dataset and Metrics. Most of the example studies forunsupervised pretraining (learning encoder network f without labels) aredone using the ImageNet ILSVRC-2012 dataset (Russakovsky et al, 2015).The pretrained results are also tested on a wide range of datasets fortransfer learning. To evaluate the learned representations, a linearevaluation protocol is followed where a linear classifier is trained ontop of the frozen base network, and test accuracy is used as a proxy forrepresentation quality. Beyond linear evaluation, comparisons are alsomade against state-of-the-art on semi-supervised and transfer learning.

Example Default Setting. Unless otherwise specified, for dataaugmentation in the example empirical experiments described herein,random crop and resize (with random flip), color distortions, andGaussian blur are used; a ResNet-50 is used as the base encoder network;and a 2-layer MLP projection head is used to project the representationto a 128-dimensional latent space. As the loss, NT-Xent is used,optimized using LARS with linear learning rate scaling (i.e.LearningRate = 0.3 × BatchSize/256) and weight decay of 10⁻⁸. Trainingis performed at batch size 4096 for 100 epochs. Furthermore, linearwarmup is used for the first 10 epochs and the learning rate is decayedwith the cosine decay schedule without restarts.

Example Data Augmentation Techniques for Contrastive RepresentationLearning

Data augmentation defines predictive tasks. Data augmentation has notbeen considered as a systematic way to define the contrastive predictiontask. Many existing approaches define contrastive prediction tasks bychanging the architecture Hjelm et al. (2018) and Bachman et al. (2019)achieve global-to-local view prediction via constraining the receptivefield in the network architecture, whereas Oord et al. (2018) and Hénaffet al. (2019) achieve neighboring view prediction via a fixed imagesplitting procedure and a context aggregation network. However, thesecustom architectures add additional complexity and reduce theflexibility and/or applicability of the resulting model.

The techniques described herein can avoid this complexity by performingsimple random cropping (with resizing) of target images, which creates afamily of predictive tasks subsuming the above mentioned existingapproaches. FIGS. 3A and 3B demonstrate this principle. FIG. 3A showsglobal and local views while FIG. 3B shows adjacent views. Specifically,solid rectangles are images, dashed rectangles are random crops. Byrandomly cropping images, the proposed systems can sample contrastiveprediction tasks that include global to local view (B→A) or adjacentview (D→C) prediction.

This simple design choice conveniently decouples the predictive taskfrom other components such as the neural network architecture. Broadercontrastive prediction tasks can be defined by extending the family ofaugmentations and composing them stochastically.

Composition of Data Augmentation Operations Is Crucial for Learning GoodRepresentations

To systematically study the impact of data augmentation, severaldifferent augmentations were considered and can optionally be includedin implementations of the present disclosure. One example type ofaugmentation involves spatial/geometric transformation of data, such ascropping and resizing (with horizontal flipping), rotation, and cutout.Another example type of augmentation involves appearance transformation,such as color distortion (including color dropping, brightness,contrast, saturation, hue), Gaussian blur, and Sobel filtering. FIG. 4visualizes the augmentations were considered and can optionally beincluded in implementations of the present disclosure, which include thefollowing examples visualized relative to the original image: crop andresize; crop, resize (and flip); color distortion (drop); colordistortion (jitter); rotate; cutout; Gaussian noise; Gaussian blur; andSobel filtering.

To understand the effects of individual data augmentations and theimportance of augmentation composition, the performance of the proposedframework was evaluated when applying augmentations individually or inpairs. Since ImageNet images are of different sizes, exampleimplementations used for evaluation consistently apply crop and resizeimages, which makes it difficult to study other augmentations in theabsence of cropping. To eliminate this confound, an asymmetric datatransformation setting was considered for this ablation. Specifically,the example implementations always first randomly crop images and resizethem to the same resolution, and then apply the targetedtransformation(s) only to one branch of the framework in FIG. 2A, whileleaving the other branch as the identity (i.e. t(x_(t)) = x_(t)). Notethat this asymmetric data augmentation hurts the performance.Nonetheless, this setup should not substantively change the impact ofindividual data augmentations or their compositions.

FIG. 5 shows linear evaluation results under individual and compositionof transformations. In particular, FIG. 5 shows linear evaluation(ImageNet top-1 accuracy) under individual or composition of dataaugmentations, applied only to one branch. For all columns by the last,diagonal entries correspond to single transformation, and off-diagonalscorrespond to composition of two transformations (applied sequentially).The last column reflects the average over the row.

It can be observed from FIG. 5 that no single transformation suffices tolearn excellent representations, even though the model can almostperfectly identify the positive pairs in the contrastive task. Whencomposing augmentations, the contrastive prediction task becomes harder,but the quality of representation improves dramatically.

One composition of augmentations stands out: random cropping and randomcolor distortion. One explanation is as follows: one serious issue whenusing only random cropping as data augmentation is that most patchesfrom an image share a similar color distribution. FIGS. 6A and 6B showsthat color histograms alone suffice to distinguish images. Neural netsmay exploit this shortcut to solve the predictive task. Therefore, it isimportant to compose cropping with color distortion in order to learngeneralizable features.

Specifically, FIGS. 6A and 6B show histograms of pixel intensities (overall channels) for different crops of two different images (i.e., tworows). FIG. 6A is without color distortion. FIG. 6B is with colordistortion. The image for the first row is from FIG. 4 . All axes havethe same range.

Contrastive Learning Benefits From Stronger Data Augmentation ThanSupervised Learning

To further demonstrate the importance of the color augmentation, thestrength of color augmentation as adjusted as shown in Table 1. Strongercolor augmentation substantially improves the linear evaluation of thelearned unsupervised models. In this context, AutoAugment (Cubuk et al.,2019), a sophisticated augmentation policy found using supervisedlearning, does not work better than simple cropping + (stronger) colordistortion. When training supervised models with the same set ofaugmentations, it was observed that stronger color augmentation does notimprove or even hurts their performance. Thus, these experiments showthat unsupervised contrastive learning benefits from stronger (color)data augmentation than supervised learning. As such, data augmentationthat does not yield accuracy benefits for supervised learning can stillhelp considerably with contrastive learning.

TABLE 1 Color distortion strength Methods ⅛ ¼ ½ 1 1 (+Blur) AutoAugSimCLR 59.6 61.0 62.6 63.2 64.5 61.1 Supervised 77.0 76.7 76.5 75.7 75.477.1

Top-1 accuracy of unsupervised ResNet-50 using linear evaluation andsupervised ResNet-50, under varied color distortion strength and otherdata transformations. Strength 1 (+Blur) is one example default dataaugmentation policy.

Example Data Augmentation Details

Some example options for performing data augmentation operations areprovided. Other options or details can be used additionally oralternatively to these specific example details.

Example Random Crop and Resize to 224×224: A crop of random size(uniform from 0.08 to 1.0 in area) of the original size and a randomaspect ratio (default: of ¾ to 4/3) of the original aspect ratio ismade. This crop is resized to the original size. In someimplementations, the random crop (with resize) is followed by a randomhorizontal/left-to-right flip with some probability (e.g., 50%). This ishelpful but not essential. By removing this from the example defaultaugmentation policy, the top-1 linear evaluation drops from 64.5% to63.4% for our ResNet-50 model trained in 100 epochs.

Example Color Distortion Color distortion is composed by color jitteringand color dropping. Stronger color jittering usually helps, so astrength parameter can be used. One example pseudo-code for an examplecolor distortion operation using TensorFlow is as follows.

-   import tensorflow as tf-   def color _distortion(image, s=1.0):    -   # image is a tensor with value range in [0, 1].    -   # s is the strength of color distortion.-   def color _jitter(x):    -   # one can also shuffle the order of following augmentations    -   # each time they are applied.    -   x = tf.image.random _brightness (x, max_delta=0.8*s)    -   x = tf.image.random_contrast(x, lower=1-0.8*s, upper=1+0.8*s)    -   x = tf.image.random_saturation(x, lower=1-0.8*s, upper=1+0.8*s)    -   x = tf.image.random_hue(x, max_delta=0.2*s)    -   x = tf.clip_by_value(x, 0, 1)    -   return x-   def color _drop (x) :    -   image = tf.image.rgb_to grayscale(image)    -   image = tf.tile(image, [1, 1, 3])-   # randomly apply transformation with probability p.    -   image = random _apply (color_jitter, image, p=0.8)    -   image = random _apply (color_drop, image, p=0.2)    -   return image

One example pseudo-code for an example color distortion operation usingPytorch is as follows.

-   from torchvision import transforms-   def get_color_distortion (s=1.0) :    -   # s is the strength of color distortion.    -   color_jitter = transforms.ColorJitter(0.8*s, 0.8*s, 0.8*s,        0.2*s)    -   rnd_color_jitter = transforms.RandomApply([color_jitter], p=0.8)    -   rnd_gray = transforms.RandomGrayscale (p=0.2)    -   color_distort = transforms.Compose([ rnd_color_jitter, rnd_gray]        )    -   return color _distort

Example Gaussian blur This augmentation is helpful, as it improves theResNet-50 trained for 100 epochs from 63.2% to 64.5%. The image can beblurred with some probability (e.g., 50% of the time) using a Gaussiankernel. A random sample σ ∈ [0.1,2.0] can be obtained, and the kernelsize can be set to be some percentage (e.g., 10%) of the imageheight/width.

Example Architectures for the Base Encoder and the Projection HeadUnsupervised Contrastive Learning Benefits (More) From Bigger Models

FIG. 7 shows that increasing depth and width both improve performance.While similar findings hold for supervised learning, the gap betweensupervised models and linear classifiers trained on unsupervised modelsshrinks as the model size increases, suggesting that unsupervisedlearning benefits more from bigger models than its supervisedcounterpart.

Specifically, FIG. 7 shows linear evaluation of models with varied depthand width. Models in blue dots are example implementations of thepresent disclosure trained for 100 epochs, models in red starts areexample implementations of the present disclosure trained for 1000epochs, and models in green crosses are supervised ResNets trained for90 epochs. Training longer does not improve supervised ResNets.

A Nonlinear Projection Head Improves the Representation Quality of theLayer Before It

Another example aspect evaluates the importance of including aprojection head, i.e. g(h). FIG. 8 shows linear evaluation results usingthree different architectures for the head: (1) identity mapping; (2)linear projection; and (3) the default nonlinear projection with oneadditional hidden layer (and ReLU activation). Specifically, FIG. 8shows linear evaluation of representations with different projectionheads g and various dimensions of z =g(h). The representation h (beforeprojection) is 2048-dimensional here.

It can be observed that a nonlinear projection is better than a linearprojection (+3%), and much better than no projection (>10%). When aprojection head is used, similar results are observed regardless ofoutput dimension. Furthermore, even when nonlinear projection is used,the layer before the projection head, h, is still much better (>10%)than the layer after, z = g(h), which shows that the hidden layer beforethe projection head is a better representation than the layer after.

One explanation of this phenomenon is that the importance of using therepresentation before the nonlinear projection is due to loss ofinformation induced by the contrastive loss. In particular, z = g(h) istrained to be invariant to data transformation. Thus, g can removeinformation that may be useful for the downstream task, such as thecolor or orientation of objects. By leveraging the nonlineartransformation g(•), more information can be formed and maintained in h.To verify this hypothesis, experiments were conducted that use either hor g(h) to learn to predict the transformation applied during thepretraining. Here it was set g(h) = W⁽²⁾σ(W⁽¹⁾h), with the same inputand output dimensionality (i.e. 2048). Table 2 shows h contains muchmore information about the transformation applied, while g(h) losesinformation.

TABLE 2 What to predict? Random guess Representation h g(h) Color vsgrayscale 80 99.3 97.4 Rotation 25 67.6 25.6 Orig. vs corrupted 50 99.559.6 Orig. vs Sobel filtered 50 96.6 56.3

Table 2 shows the accuracy of training additional MLPs on differentrepresentations to predict the transformation applied. Other than cropand color augmentation, rotation (one of {0,90,180,270}), Gaussiannoise, and Sobel filtering transformation were additionally andindependently added during the pretraining for the last three rows. Bothb and g(h) are of the same dimensionality, i.e. 2048.

Example Loss Functions and Batch Size Normalized Cross Entropy Loss WithAdjustable Temperature Works Better Than Alternatives

Additional example experiments compared the NT-Xent loss against othercommonly used contrastive loss functions, such as logistic loss (Mikolovet al., 2013), and margin loss (Schroff et al., 2015). FIG. 9 shows theobjective function as well as the gradient to the input of the lossfunction. Specifically, FIG. 9 shows negative loss functions and theirgradients. All input vectors i.e. u, v⁺, v⁻, are ℓ₂ normalized. NT-Xentis an abbreviation for “Normalized Temperature-scaled Cross Entropy”.Different loss functions impose different weightings of positive andnegative examples.

Looking at the gradient, it can be observed that: 1) ℓ₂ normalizationalong with temperature effectively weights different examples, and anappropriate temperature can help the model learn from hard negatives;and 2) unlike cross-entropy, other objective functions do not weigh thenegatives by their relative hardness. As a result, one must applysemi-hard negative mining (Schroff et al., 2015) for these lossfunctions: instead of computing the gradient over all loss terms, onecan compute the gradient using semi-hard negative terms (i.e., thosethat are within the loss margin and closest in distance, but fartherthan positive examples).

To make the comparisons fair, the same ℓ₂ normalization was used for allloss functions, and we tune the hyperparameters, and report their bestresults. Table 3 shows that, while (semi-hard) negative mining helps,the best result is still much worse than NT-Xent loss.

TABLE 3 Margin NT-Logi. Margin (sh) NT-Logi.(sh) NT-Xent 50.9 51.6 57.557.9 63.9

Linear evaluation (top-1) for models trained with different lossfunctions. “sh” means using semi-hard negative mining.

Another example set of experiments tested the importance of the ℓ₂normalization and temperature τ in the NT-Xent loss. Table 4 shows thatwithout normalization and proper temperature scaling, performance issignificantly worse. Without ℓ₂ normalization, the contrastive taskaccuracy is higher, but the resulting representation is worse underlinear evaluation.

TABLE 4 ℓ₂ norm? τ Entropy Contrastive acc. Top 1 Yes 0.05 1.0 90.5 59.70.1 4.5 87.8 64.4 0.5 8.2 68.2 60.7 1 8.3 59.1 58.0 No 10 0.5 91.7 57.2100 0.5 2.1 57.0

Linear evaluation for models trained with different choices of ℓ₂ normand temperature r for NT-Xent loss. The contrastive distribution is over4096 examples.

Contrastive Learning Benefits (More) From Larger Batch Sizes and LongerTraining

FIG. 10 shows the impact of batch size when models are trained fordifferent numbers of epochs. In particular, FIG. 10 provides data forlinear evaluation models (ResNet-50) trained with different batch sizeand epochs. Each bar is a single run from scratch.

When the number of training epochs is small (e.g. 100 epochs), largerbatch sizes have a significant advantage over the smaller ones. Withmore training steps/epochs, the gaps between different batch sizesdecrease or disappear, provided the batches are randomly resampled. Incontrast to supervised learning, in contrastive learning, larger batchsizes provide more negative examples, facilitating convergence (i.e.taking fewer epochs and steps for a given accuracy). Training longeralso provides more negative examples, improving the results.

Comparison With State-of-the-Art

In this section, example experiments are described in which ResNet-50 isused in 3 different hidden layer widths (width multipliers of ^(1×),^(2 ×), and ⁴ ^(×)). For better convergence, the models here are trainedfor 1000 epochs.

Linear evaluation. Table 5 compares example results with previousapproaches (Zhuang et al., 2019; He et al., 2019a; Misra & van derMaaten, 2019; Hénaff et al., 2019; Kolesnikov et al., 2019; Donahue &Simonyan, 2019; Bachman et al., 2019; Tian et al., 2019) in the linearevaluation setting. FIG. 1 also shows comparisons among differentmethods. Standard networks are able to be used to obtain substantiallybetter results compared to previous methods that require specificallydesigned architectures. The best result obtained with the proposedResNet-50 (⁴ ^(×)) can match the supervised pretrained ResNet-50.

TABLE 5 Method Architecture Param. Top 1 Top 5 Methods using ResNet-50:Local Agg. ResNet-50 24 60.2 - MoCo ResNet-50 24 60.6 - PIRL ResNet-5024 63.6 - CPC v2 ResNet-50 24 63.8 85.3 SimCLR (ours) ResNet-50 24 69.389.0 Methods using other architectures: Rotation RevNet-50 (4 ×) 8655.4 - BigBiGAN RevNet-50 (4 ×) 86 61.3 81.9 AMDIM Custom-ResNet 62668.1 - CMC ResNet-50 (2 ×) 188 68.4 88.2 MoCo ResNet-50 (4 ×) 375 68.6 -CPC v2 ResNet-161 (•) 305 71.5 90.1 SimCLR (ours) ResNet-50 (2 ×) 9474.2 92.0 SimCLR (ours) ResNet-50 (4 ×) 375 76.5 93.2

ImageNet accuracies of linear classifiers trained on representationslearned with different self-supervised methods.

Semi-supervised learning. In some examples, 1% or 10% of the labeledILSVRC-12 training datasets can be sampled in a class-balanced way (i.e.around 12.8 and 128 images per class respectively). The whole basenetwork can be fine-tuned on the labeled data without regularization.Table 6 shows the comparisons of the results against recent methods(Zhai et al., 2019; Xie et al., 2019; Sohn et al., 2020; Wu et al.,2018; Donahue & Simonyan, 2019; Misra & van der Maaten, 2019; Hénaff etal., 2019). Again, the proposed approach significantly improves overstate-of-the-art with both 1% and 10% of the labels.

TABLE 6 Method Architecture Label fraction 1% 10% Top 5 Methods usingother label-propagation: Pseudo-label ResNet50 51.6 82.4 VAT+EntropyMin. ResNet50 47.0 83.4 UDA (w. RandAug) ResNet50 - 88.5 FixMatch (w.RandAug) ResNet50 - 89.1 S4L (Rot+VAT+En. M.) ResNet50 (4×) - 91.2Methods using representation learning only: InstDisc ResNet50 39.2 77.4BigBiGAN RevNet-50 (4 ×) 55.2 78.8 PIRL ResNet-50 57.2 83.8 CPC v2ResNet-161 (•) 77.9 91.2 SimCLR (ours) ResNet-50 75.5 87.8 SimCLR (ours)ResNet-50 (2 ×) 83.0 91.2 SimCLR (ours) ResNet-50 (4 ×) 85.8 92.6

ImageNet accuracy of models trained with few labels.

Transfer learning. Transfer learning performance was also evaluatedacross 12 natural image datasets in both linear evaluation (fixedfeature extractor) and fine-tuning settings. Hyperparameter tuning wasperformed for each model-dataset combination and the besthyperparameters on a validation set were selected. Table 8 shows resultswith the ResNet-50 ( ⁴ ^(×)) model. When fine-tuned, the proposedself-supervised model significantly outperforms the supervised baselineon 5 datasets, whereas the supervised baseline is superior on only 2(i.e. Pets and Flowers). On the remaining 5 datasets, the models arestatistically tied.

TABLE 7 Foo d CIFAR1 0 CIFAR10 0 Birdsna p SUN39 7 Car s Aircraf tVOC200 7 DT D Pet s Caltech -101 Flower s Linear evaluation: SimCLR(ours) 76.9 95.3 80.2 48.4 65.9 60.0 61.2 84.2 78.9 89.2 93.9 95.0Supervise d 75.2 95.7 81.2 56.4 64.9 68.8 63.8 83.8 78.7 92.3 94.1 94.2Fine-tuned: SimCLR (ours) 89.4 98.6 89.0 78.2 68.1 92.1 87.0 86.6 77.892.1 94.1 97.6 Supervise d 88.7 98.3 88.7 77.8 67.0 91.4 88.0 86.5 78.893.2 94.2 98.0 Random init 88.3 96.0 81.9 77.0 53.7 91.3 84.8 69.4 64.182.7 72.5 92.5

Comparison of transfer learning performance of our self-supervisedapproach with supervised baselines across 12 natural imageclassification datasets, for ResNet-50 (4 ×) models pretrained onImageNet. Results not significantly worse than the best (p > 0.05,permutation test) are shown in bold.

Example Devices and Systems

FIG. 14A depicts a block diagram of an example computing system 100according to example embodiments of the present disclosure. The system100 includes a user computing device 102, a server computing system 130,and a training computing system 150 that are communicatively coupledover a network 180.

The user computing device 102 can be any type of computing device, suchas, for example, a personal computing device (e.g., laptop or desktop),a mobile computing device (e.g., smartphone or tablet), a gaming consoleor controller, a wearable computing device, an embedded computingdevice, or any other type of computing device.

The user computing device 102 includes one or more processors 112 and amemory 114. The one or more processors 112 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 114can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 114 can store data 116and instructions 118 which are executed by the processor 112 to causethe user computing device 102 to perform operations.

In some implementations, the user computing device 102 can store orinclude one or more machine-learned models 120. For example, themachine-learned models 120 can be or can otherwise include variousmachine-learned models such as neural networks (e.g., deep neuralnetworks) or other types of machine-learned models, including non-linearmodels and/or linear models. Neural networks can include feed-forwardneural networks, recurrent neural networks (e.g., long short-term memoryrecurrent neural networks), convolutional neural networks or other formsof neural networks. Example machine-learned models 120 are discussedwith reference to FIGS. 2A-B.

In some implementations, the one or more machine-learned models 120 canbe received from the server computing system 130 over network 180,stored in the user computing device memory 114, and then used orotherwise implemented by the one or more processors 112. In someimplementations, the user computing device 102 can implement multipleparallel instances of a single machine-learned model 120.

Additionally or alternatively, one or more machine-learned models 140can be included in or otherwise stored and implemented by the servercomputing system 130 that communicates with the user computing device102 according to a client-server relationship. For example, themachine-learned models 140 can be implemented by the server computingsystem 140 as a portion of a web service (e.g., a visual analysisservice). Thus, one or more models 120 can be stored and implemented atthe user computing device 102 and/or one or more models 140 can bestored and implemented at the server computing system 130.

The user computing device 102 can also include one or more user inputcomponent 122 that receives user input. For example, the user inputcomponent 122 can be a touch-sensitive component (e.g., atouch-sensitive display screen or a touch pad) that is sensitive to thetouch of a user input object (e.g., a finger or a stylus). Thetouch-sensitive component can serve to implement a virtual keyboard.Other example user input components include a microphone, a traditionalkeyboard, or other means by which a user can provide user input.

The server computing system 130 includes one or more processors 132 anda memory 134. The one or more processors 132 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 134can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 134 can store data 136and instructions 138 which are executed by the processor 132 to causethe server computing system 130 to perform operations.

In some implementations, the server computing system 130 includes or isotherwise implemented by one or more server computing devices. Ininstances in which the server computing system 130 includes pluralserver computing devices, such server computing devices can operateaccording to sequential computing architectures, parallel computingarchitectures, or some combination thereof.

As described above, the server computing system 130 can store orotherwise include one or more machine-learned models 140. For example,the models 140 can be or can otherwise include various machine-learnedmodels. Example machine-learned models include neural networks or othermulti-layer non-linear models. Example neural networks include feedforward neural networks, deep neural networks, recurrent neuralnetworks, and convolutional neural networks. Example models 140 arediscussed with reference to FIGS. 2A-B.

The user computing device 102 and/or the server computing system 130 cantrain the models 120 and/or 140 via interaction with the trainingcomputing system 150 that is communicatively coupled over the network180. The training computing system 150 can be separate from the servercomputing system 130 or can be a portion of the server computing system130.

The training computing system 150 includes one or more processors 152and a memory 154. The one or more processors 152 can be any suitableprocessing device (e.g., a processor core, a microprocessor, an ASIC, aFPGA, a controller, a microcontroller, etc.) and can be one processor ora plurality of processors that are operatively connected. The memory 154can include one or more non-transitory computer-readable storagemediums, such as RAM, ROM, EEPROM, EPROM, flash memory devices, magneticdisks, etc., and combinations thereof. The memory 154 can store data 156and instructions 158 which are executed by the processor 152 to causethe training computing system 150 to perform operations. In someimplementations, the training computing system 150 includes or isotherwise implemented by one or more server computing devices.

The training computing system 150 can include a model trainer 160 thattrains the machine-learned models 120 and/or 140 stored at the usercomputing device 102 and/or the server computing system 130 usingvarious training or learning techniques, such as, for example, backwardspropagation of errors. For example, a loss function can bebackpropagated through the model(s) to update one or more parameters ofthe model(s) (e.g., based on a gradient of the loss function). Variousloss functions can be used such as mean squared error, likelihood loss,cross entropy loss, hinge loss, and/or various other loss functions suchas those contained in FIG. 9 . Gradient descent techniques can be usedto iteratively update the parameters over a number of trainingiterations.

In some implementations, performing backwards propagation of errors caninclude performing truncated backpropagation through time. The modeltrainer 160 can perform a number of generalization techniques (e.g.,weight decays, dropouts, etc.) to improve the generalization capabilityof the models being trained.

In particular, the model trainer 160 can train the machine-learnedmodels 120 and/or 140 based on a set of training data 162. The trainingdata 162 can include, for example, data of different modalities such asimagery, audio samples, text, and/or the like. Example types of imagesthat can be used include video frames, LiDAR point clouds, X-ray images,computed tomography scans, hyper-spectral images, and/or various otherforms of imagery.

In some implementations, if the user has provided consent, the trainingexamples can be provided by the user computing device 102. Thus, in suchimplementations, the model 120 provided to the user computing device 102can be trained by the training computing system 150 on user-specificdata received from the user computing device 102. In some instances,this process can be referred to as personalizing the model.

The model trainer 160 includes computer logic utilized to providedesired functionality. The model trainer 160 can be implemented inhardware, firmware, and/or software controlling a general purposeprocessor. For example, in some implementations, the model trainer 160includes program files stored on a storage device, loaded into a memoryand executed by one or more processors. In other implementations, themodel trainer 160 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM hard disk or optical or magnetic media. The modeltrainer can be configured to perform any of the contrastive learningtechniques described herein.

The network 180 can be any type of communications network, such as alocal area network (e.g., intranet), wide area network (e.g., Internet),or some combination thereof and can include any number of wired orwireless links. In general, communication over the network 180 can becarried via any type of wired and/or wireless connection, using a widevariety of communication protocols (e.g., TCP/IP, HTTP, SMTP, FTP),encodings or formats (e.g., HTML, XML), and/or protection schemes (e.g.,VPN, secure HTTP, SSL).

FIG. 11 depicts a flow diagram of an example method 1100 for performingsemi-supervised contrastive learning of visual representations accordingto the examples of the present disclosure. Although FIG. 11 depictssteps performed in a particular order for purposes of illustration anddiscussion, the methods of the present disclosure are not necessarilylimited to the particularly illustrated order or arrangement.

The various steps of the method 1100 can be omitted, rearranged,combined, and/or adapted in various ways without deviating from thescope of the present disclosure. Further, the operations and featuresdescribed with respect to FIG. 11 also may be performed by one or morecomputing devices of a computing system and/or by one or more processingdevices executing computer-readable instructions provided via anon-transitory computer-readable medium.

Method 1100 begins at block 1102 when, for example, a computer systemperforms contrastive learning based on a set of training data. In anexample, the computer system performs contrastive learning based on oneor more of the various examples provided in the present disclosure. Forexample, the computer system may perform contrastive learning based onexample framework 200 and other examples provided throughout the presentdisclosure.

In an example, the computer system performs unsupervised pretraining ofa model using contrastive learning based on a set of unlabeled trainingdata. For example, the computer system may pretrain a large,task-agnostic general convolutional network using a large number ofunlabeled training data. In various examples, training data generallymay include any type of visual and non-visual data including, but notlimited to, images, video content, image frames of video content, audiodata, textual data, geospatial data, sensor data, etc. Unlabeledtraining data generally refers to any data where labels, descriptions,features, and/or properties are not provided or otherwise have beendeleted, discarded or fully ignored. In an example, pretraining of amodel may be performed using unsupervised or self-supervised contrastivelearning based on unlabeled, task agnostic training data without classlabels and without being directed or tailored to a specificclassification task.

In an example, the computer system performs unsupervised pretraining ofa large model using a modified version SimCLR. For example, where insome examples, SimCLR training generally may involve ResNet-50 (4x)models, the computer system generally performs unsupervised pretrainingof larger models with increased depth and width, such as a 152-layerResNet with 3x wider channels and selective kernels, a channel-wiseattention mechanism that improves parameter efficiency, performance, andaccuracy. In some examples, unsupervised pretraining of larger modelsmay include ResNet variants, such as ResNet-D or other variations.Further, pretraining may be performed using a projection head neuralnetwork having three or more layers on top of a ResNet encoder or otherencoder, such as base encoder neural network 204. In an example,capacity of a projection head neural network, such as projection headneural network 206 may be increased by making it deeper. For example, aprojection head neural network may include three or more layers, aportion of which may be later reused during fine-tuning anddistillation, instead discarding the projection head neural networkentirely after pretraining.

At block 1104, the computing system generates an image classificationmodel with one or more layers of a projection head neural network usedin the contrastive learning. In an example, a computer system generatesor otherwise configures an image classification model or another type ofclassification model that has been pretrained based on a set ofunlabeled training data. For example, the computer system may generateor configure a pretrained image classification model that has beenpretrained in accordance with examples at block 1102 and throughout thepresent disclosure. In various examples, the computing system generatesor configures a classification model for fine-tuning that includes somebut not all of multiple projection head neural network layers that havebeen pretrained using contrastive learning with unlabeled training data,as further described with respect to FIG. 12 and in other examples ofthe present disclosure.

FIG. 12 depicts an example graphical diagram 1200 for performingfine-tuning of a classification model with one or more projection headneural network layers that have been pretrained using contrastivelearning, according to example embodiments of the present disclosure.Example graphical diagram 1200 includes fine-tuning input data 1202,classification model 1204, and classification output 1212. In anexample, fine-tuning input data 1202, such as labeled training data, isused to fine-tune classification model 1204 during a fine-tuning phasebased on classification output 1212. Classification model 1204 furtherincludes network 1206, projection head layer(s) 1208, and classificationhead 1210.

In an example, classification model 1204 includes a network 1206, suchas a task-agnostic network that has been pretrained with contrastivelearning using unlabeled training data (e.g., a pretrained base encoderneural network, large convolutional neural network, etc.).Classification model 1204 also reuses a portion of multiple layers of aprojection head neural network that also was pretrained with contrastivelearning using unlabeled training data (i.e., projection head layer(s)1208). For example, instead of discarding a projection head neuralnetwork (e.g., projection head neural network 206) entirely afterpretraining, a portion of the layers of the projection head neuralnetwork (i.e., projection head layer(s) 1208) may be retained andincorporated with the pretrained based encoder neural network duringfine-tuning. In addition, classification head 1210 generally may receiveand process one or more representations to generate classificationoutput 1212, such as a classification prediction, detection prediction,recognition prediction, segmentation prediction, and/or other types ofpredictions and prediction tasks.

In an example, a three-layer projection head neural network, g(h_(i)) =W⁽³⁾(σ(W⁽²⁾σ(W⁽¹⁾h_(i))) may be used where σ is a ReLU non-linearity(bias not shown), for example, instead of usingƒ^(task)(x_(i))=W^(task)ƒ (x_(i)) to compute the logits of pre-definedclasses where W^(task) is the weight for an added task-specific linearlayer (bias also not shown). As such, fine-tuning may be performed usinga non-input, middle layer of the projection head neural network ratherthan an input layer based on a new encoder function: ƒ^(task)(x_(i)) =W^(task)σ(W⁽¹⁾ƒ (X_(i))).

At block 1106, the computing system performs fine-tuning of the imageclassification model based on a set of labeled training data. In anexample, the computer system fine-tunes a model already pretrained usinga set of unlabeled training data. For example, the computer system mayperform fine-tuning of a pretrained classification model 1204 based on aset of fine-tuning input data 1202 comprising a relatively small numberor proportion of labeled training data (e.g., 1%, 5%, 10%) as comparedto a number unlabeled pretraining samples. In various examples, labeledtraining data generally refers to a set of one or more training datasamples that have been associated or tagged with one or more labels,which may include descriptions, features, and/or properties. In someexamples, classification model 1204 is fine-tuned with a small fractionof data having class labels, allowing internal representations to beadjusted for one or more specific tasks.

In an example, classification model 1204 obtains or otherwise receives aset of labeled, fine-tuning input data 1202. In various examples,labeled fine-tuning input data 1202 is processed by network 1206 andprojection head layer(s) 1208. For example, network 1206 generally maybe a task-agnostic, pretrained network that has been pretrained usingcontrastive learning with unlabeled training data. In addition, aportion of projection head layer(s) 1208 from a projection head neuralnetwork that also has been trained using the contrastive learning withthe unlabeled training data may be reused instead of being discardedentirely after the pretraining.

For example, some but not all pretrained projection head layer(s) 1208may be added as one or more respective linear transformation layers ontop of a pretrained network (e.g., network 1206), which in some examplesmay be a pretrained base encoder neural network 204. As such,fine-tuning of classification model 1204 may be performed by adjustingvarious parameters based on labeled fine-tuning input data 1202, forexample, using a supervised cross-entropy loss or other type of lossfunction (not shown), allowing classification model 1204 to slightlyadjust internal representations for one or more specific tasks. In someexamples, projection head layer(s) 1208 comprise one or more but not allof a set of pretrained projection head neural network layers. Suchprojection head layer(s) 1208 may include one or more non-input layers,such as, a non-input first layer or other middle layer, of a pretrainedprojection head neural network.

At block 1108, the computing system performs distillation training usingthe unlabeled training data from the contrastive learning, where thefine-tuned classification model is distilled to a comparatively smallerstudent model. In various examples, the computing system performsdistillation training by reusing the unlabeled training data that waspreviously used during the contrastive learning pretraining. Forexample, the computing system may reuse the unlabeled pretraining datadirectly when performing distillation as part of training a lightweightstudent network specialized for one or more targeted tasks. As such, theunlabeled training data first is used in a task-agnostic fashion forpretraining and then again used in distillation after performingfine-tuning to train a student network for one or more specializedtargeted tasks. Examples of distillation training may be described withrespect to FIG. 13 .

FIG. 13 depicts an example graphical diagram 1300 for performingdistillation training of a student model based on a fine-tunedclassification model with one or more projection head neural networklayers that have been pretrained using contrastive learning, accordingto example embodiments of the present disclosure. Example graphicaldiagram 1300 includes distillation input data 1302, classification model1304, network 1306, projection head layer(s) 1308, classification head1310, classification output 1312, student network 1314, studentclassification output 1316, and distillation loss 1318.

In an example, the computing system obtains or otherwise receivesdistillation input data 1302. Distillation input data 1302 generally mayinclude some or all of the unlabeled data used in pretraining of amodel. As such, in various examples, unlabeled distillation input data1302 was first used when pretraining a model in a task-agnostic fashionand then again reused after performing fine-tuning of the model todistill the fine-tuned model to a student specialized in one or moretasks.

In an example, unlabeled distillation input data 1302 is provided toclassification model 1304 and student network 1314 for processing.Classification model 1304 may be an image classification model or anyother type of classification model. In various examples, classificationmodel 1304 is a pretrained and fine-tuned classification model. Forexample, classification model 1304 may be pretrained and fine-tunedaccording to one or more of the various examples provided in the presentdisclosure.

In an example, classification model 1304 includes a fine-tuned network1306, such as a network (e.g., a fine-tuned base encoder neural network,large convolutional neural network, etc.) that was first pretrained withcontrastive learning using unlabeled training data and later fine-tunedbased on a relatively small set of labeled training data. Classificationmodel 1304 also includes one or more projection head layer(s) 1308, forexample, originally from a projection head neural network pretrainedwith contrastive learning using unlabeled training data, where thespecific projection head layer(s) were preserved after the pretrainingand later fine-tuned based on the set of label training data. In variousexamples, fine tuning of classification model 1304, network 1306, andprojection head layer(s) 1308 generally may be performed in accordancewith examples discussed at block 1106 and throughout the presentdisclosure. Further, classification head 1310 may receive and processone or more representations to generate classification output 1312, suchas a classification prediction, detection prediction, recognitionprediction, segmentation prediction, and/or other types of predictionsand prediction tasks.

In an example, classification model 1304 is used to train a studentnetwork 1314 that is more specialized for a target task. For example,fine-tuned classification model 1304 is used when performingdistillation training to distill the model to student network 1314comprising a relatively smaller number of parameters relative to imageclassification model. As such, student network 1314 generally islightweight and better suited to be deployed to client computing deviceswith limited local computing resources. For example, student network1314 may be deployed for use on one or more various different types ofclient computing devices including, but not limited to, mobile devices,Internet of Things (IoT) edge devices, or any other client devices wheredata is processed locally instead of being transmitted for remoteprocessing. In various examples, student network 1314 obtains orotherwise receives and processes unlabeled distillation input data 1302to generate student classification output 1316.

In an example, unlabeled data from a contrastive learning pretrainingphase is reused to train student network 1314 for a target task. In someexamples, a fine-tuned classification model 1304 provides labels fortraining student network 1314 and distillation loss 1318 may beminimized based on:

$L^{\text{distill}} = - {\sum\limits_{x_{i} \in D}\left\lbrack {\sum\limits_{y}{P^{T}\left( {y\left| x_{i} \right);\tau} \right)\log P^{S}\left( {y\left| x_{i} \right);\tau} \right)}} \right\rbrack}$

Where

P(y|x_(i))) = exp (f^(task)(x_(i))[y]/τ)/∑_(y^(′))exp (f^(task)(x_(i))[y^(′)]/τ),

and τ is a temperature scalar.

In addition, a teacher network (i.e., classification model 1304) thatoutputs P^(T)(yx_(i)) can be fixed during the distillation, so onlystudent network 1314 is trained.

In some examples when distillation training involves labeled trainingdata, distillation loss 1318 may be combined with ground-truth labeledexamples using a weighted combination as follows.

$\begin{array}{l}{L = - \left( {1 - \alpha} \right){\sum\limits_{{({x_{i},y_{i}})} \in D^{L}}\left\lbrack {\log P^{S}\left( {y_{i}\left| x_{i} \right)} \right)} \right\rbrack} -} \\{\alpha{\sum\limits_{x_{i} \in D}\left\lbrack {\sum\limits_{y}{P^{T}\left( {y\left| x_{i} \right);\tau} \right)\log P^{S}\left( {y\left| x_{i} \right);\tau} \right)}} \right\rbrack}}\end{array}$

FIG. 14A illustrates one example computing system that can be used toimplement the present disclosure. Other computing systems can be used aswell. For example, in some implementations, the user computing device102 can include the model trainer 160 and the training dataset 162. Insuch implementations, the models 120 can be both trained and usedlocally at the user computing device 102. In some of suchimplementations, the user computing device 102 can implement the modeltrainer 160 to personalize the models 120 based on user-specific data.

FIG. 14B depicts a block diagram of an example computing device 10 thatperforms according to example embodiments of the present disclosure. Thecomputing device 10 can be a user computing device or a server computingdevice.

The computing device 10 includes a number of applications (e.g.,applications 1 through N). Each application contains its own machinelearning library and machine-learned model(s). For example, eachapplication can include a machine-learned model. Example applicationsinclude a text messaging application, an email application, a dictationapplication, a virtual keyboard application, a browser application, etc.

As illustrated in FIG. 14B, each application can communicate with anumber of other components of the computing device, such as, forexample, one or more sensors, a context manager, a device statecomponent, and/or additional components. In some implementations, eachapplication can communicate with each device component using an API(e.g., a public API). In some implementations, the API used by eachapplication is specific to that application.

FIG. 14C depicts a block diagram of an example computing device 50 thatperforms according to example embodiments of the present disclosure. Thecomputing device 50 can be a user computing device or a server computingdevice.

The computing device 50 includes a number of applications (e.g.,applications 1 through N). Each application is in communication with acentral intelligence layer. Example applications include a textmessaging application, an email application, a dictation application, avirtual keyboard application, a browser application, etc. In someimplementations, each application can communicate with the centralintelligence layer (and model(s) stored therein) using an API (e.g., acommon API across all applications).

The central intelligence layer includes a number of machine-learnedmodels. For example, as illustrated in FIG. 14C, a respectivemachine-learned model (e.g., a model) can be provided for eachapplication and managed by the central intelligence layer. In otherimplementations, two or more applications can share a singlemachine-learned model. For example, in some implementations, the centralintelligence layer can provide a single model (e.g., a single model) forall of the applications. In some implementations, the centralintelligence layer is included within or otherwise implemented by anoperating system of the computing device 50.

The central intelligence layer can communicate with a central devicedata layer. The central device data layer can be a centralizedrepository of data for the computing device 50. As illustrated in FIG.14C, the central device data layer can communicate with a number ofother components of the computing device, such as, for example, one ormore sensors, a context manager, a device state component, and/oradditional components. In some implementations, the central device datalayer can communicate with each device component using an API (e.g., aprivate API).

Additional Disclosure

The technology discussed herein refers to servers, databases, softwareapplications, and other computer-based systems, as well as actionstaken, and information sent to and from such systems. The inherentflexibility of computer-based systems allows for a great variety ofpossible configurations, combinations, and divisions of tasks andfunctionality between and among components. For instance, processesdiscussed herein can be implemented using a single device or componentor multiple devices or components working in combination. Databases andapplications can be implemented on a single system or distributed acrossmultiple systems. Distributed components can operate sequentially or inparallel.

While the present subject matter has been described in detail withrespect to various specific example embodiments thereof, each example isprovided by way of explanation, not limitation of the disclosure. Thoseskilled in the art, upon attaining an understanding of the foregoing,can readily produce alterations to, variations of, and equivalents tosuch embodiments. Accordingly, the subject disclosure does not precludeinclusion of such modifications, variations and/or additions to thepresent subject matter as would be readily apparent to one of ordinaryskill in the art. For instance, features illustrated or described aspart of one embodiment can be used with another embodiment to yield astill further embodiment. Thus, it is intended that the presentdisclosure cover such alterations, variations, and equivalents.

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What is claimed is: 1-20. (canceled)
 21. A computing system to performcontrastive learning, the computing system comprising: one or moreprocessors; and one or more non-transitory computer-readable media thatcollectively store: a base encoder neural network configured to processan input to generate an intermediate representation of the input; aprojection head neural network configured to process the intermediaterepresentation of the input to generate a projected representation ofthe input; and instructions that, when executed by the one or moreprocessors, cause the computing system to perform operations, theoperations comprising: obtaining one or more training inputs; performingone or more first augmentation operations on at least one of thetraining inputs to obtain a first augmented input; separate fromperforming the one or more first augmentation operations, performing oneor more second augmentation operations on the at least one of thetraining inputs to obtain a second augmented input; wherein at least oneof the one or more second augmentation or the one or more secondaugmentation operations comprise one or both of: a random crop operationthat randomly crops the training input and a second random colordistortion operation that randomly modifies color values of the traininginput; respectively processing, with the base encoder neural network,the first augmented input and the second augmented input to respectivelygenerate a first intermediate representation for the first augmentedinput and a second intermediate representation for the second augmentedinput; respectively processing, with the projection head neural network,the first intermediate representation and the second intermediaterepresentation to respectively obtain a first projected representationfor the first augmented input and a second projected representation forthe second augmented input; evaluating a loss function that evaluates adifference between the first projected representation and the secondprojected representation; and modifying one or more values of one ormore parameters of one or both of the base encoder neural network andthe projection head neural network based at least in part on the lossfunction.
 22. The computing system of claim 21, wherein the one or morefirst augmentation operations further comprises a first resize operationthat resizes the training input.
 23. The computing system of claim 21,wherein the one or more first augmentation operations further comprisesa first random flip operation that randomly flips the training input.24. The computing system of claim 21, wherein the random colordistortion operation has a color distortion strength of at least onehalf.
 25. The computing system of claim 24, wherein the random colordistortion operation has a color distortion strength of one.
 26. Thecomputing system of claim 21, wherein the one or more first augmentationoperations further comprises a first random Gaussian blur operation thatrandomly applies a Gaussian blur to the training input.
 27. Thecomputing system of claim 21, wherein the base encoder neural networkcomprises a ResNet convolutional neural network, and wherein theintermediate representation comprises an output of a final averagepooling layer of the ResNet convolutional neural network.
 28. Thecomputing system of claim 21, wherein the projection head neural networkcomprises a multi-layer perceptron that comprises one hidden layer and arectified linear unit non-linear activation function.
 29. The computingsystem of claim 21, wherein the loss function comprises an L2 normalizedcross entropy loss with an adjustable temperature parameter.
 30. Thecomputing system of claim 29, wherein the adjustable temperatureparameter has a value equal to or greater than 0.1 and equal to or lessthan 0.5.
 31. The computing system of claim 21, wherein the operationsfurther comprise performing the operations described in claim 1 across atraining batch of training inputs, wherein the training batch oftraining inputs comprises at least 256 training inputs.
 32. Thecomputing system of claim 31, wherein the training batch comprisesgreater than 2000 training inputs.
 33. The computing system of claim 32,wherein the training batch comprises greater than 4000 training inputs.34. The computing system of claim 31, wherein the operations furthercomprise performing learning rate scaling based on a number of traininginputs included in the training batch.
 35. The computing system of claim21, wherein the operations further comprise after said modifying,generating a classification model from the base encoder neural networkand the projection head neural network, the classification modelcomprising some but not all of the plurality of layers of the projectionhead neural network; and performing fine-tuning of the classificationmodel based on a set of labeled data.
 36. The computing system of claim21, wherein evaluating the loss function comprises evaluating the lossfunction based only on in-batch negative example sampling, whereby aninstance class representation vector is not required to be stored in amemory bank.
 37. The computing system of claim 21, wherein evaluatingthe loss function comprises performing global batch normalization toaggregate mean and variance over a plurality of different devices.
 38. Acomputer-implemented method to perform contrastive learning, methodcomprising: obtaining one or more training inputs; performing one ormore first augmentation operations on at least one of the traininginputs to obtain a first augmented input; separate from performing theone or more first augmentation operations, performing one or more secondaugmentation operations on the at least one of the training inputs toobtain a second augmented input; wherein at least one of the one or moresecond augmentation or the one or more second augmentation operationscomprise one or both of: a random crop operation that randomly crops thetraining input and a second random color distortion operation thatrandomly modifies color values of the training input; respectivelyprocessing, with a base encoder neural network, the first augmentedinput and the second augmented input to respectively generate a firstintermediate representation for the first augmented input and a secondintermediate representation for the second augmented input; respectivelyprocessing, with a projection head neural network, the firstintermediate representation and the second intermediate representationto respectively obtain a first projected representation for the firstaugmented input and a second projected representation for the secondaugmented input, wherein the projection head neural network isconfigured to perform at least one non-linear transformation; evaluatinga loss function that evaluates a difference between the first projectedrepresentation and the second projected representation; and modifyingone or more values of one or more parameters of one or both of the baseencoder neural network and the projection head neural network based atleast in part on the loss function.
 39. The computer-implemented methodof claim 38, wherein the loss function comprises an L2 normalized crossentropy loss with an adjustable temperature parameter.
 40. One or morenon-transitory computer-readable media that collectively store a baseencoder neural network that has been trained by a training method, thetraining method comprising: obtaining one or more training inputs;performing one or more first augmentation operations on at least one ofthe training inputs to obtain a first augmented input; separate fromperforming the one or more first augmentation operations, performing oneor more second augmentation operations on the at least one of thetraining inputs to obtain a second augmented input; wherein at least oneof the one or more second augmentation or the one or more secondaugmentation operations comprise one or both of: a random crop operationthat randomly crops the training input and a second random colordistortion operation that randomly modifies color values of the traininginput; respectively processing, with the base encoder neural network,the first augmented input and the second augmented input to respectivelygenerate a first intermediate representation for the first augmentedinput and a second intermediate representation for the second augmentedinput; respectively processing, with a projection head neural network,the first intermediate representation and the second intermediaterepresentation to respectively obtain a first projected representationfor the first augmented input and a second projected representation forthe second augmented input, wherein the projection head neural networkis configured to perform at least one non-linear transformation;evaluating a loss function that evaluates a difference between the firstprojected representation and the second projected representation; andmodifying one or more values of one or more parameters of one or both ofthe base encoder neural network and the projection head neural networkbased at least in part on the loss function.